How to Write a Checker#

You can find some simple examples in the distribution (custom.py , custom_raw.py and deprecation_checker.py).

There are three kinds of checkers:

  • Raw checkers, which analyse each module as a raw file stream.

  • Token checkers, which analyse a file using the list of tokens that represent the source code in the file.

  • AST checkers, which work on an AST representation of the module.

The AST representation is provided by the astroid library. astroid adds additional information and methods over ast in the standard library, to make tree navigation and code introspection easier.

Writing an AST Checker#

Let's implement a checker to make sure that all return nodes in a function return a unique constant. Firstly we will need to fill in some required boilerplate:

import astroid
from astroid import nodes
from typing import TYPE_CHECKING, Optional

from pylint.checkers import BaseChecker

if TYPE_CHECKING:
    from pylint.lint import PyLinter


class UniqueReturnChecker(BaseChecker):

    name = "unique-returns"
    msgs = {
        "W0001": (
            "Returns a non-unique constant.",
            "non-unique-returns",
            "All constants returned in a function should be unique.",
        ),
    }
    options = (
        (
            "ignore-ints",
            {
                "default": False,
                "type": "yn",
                "metavar": "<y or n>",
                "help": "Allow returning non-unique integers",
            },
        ),
    )

So far we have defined the following required components of our checker:

  • A name. The name is used to generate a special configuration

    section for the checker, when options have been provided.

  • A message dictionary. Each checker is being used for finding problems

    in your code, the problems being displayed to the user through messages. The message dictionary should specify what messages the checker is going to emit. See Defining a Message for the details about defining a new message.

We have also defined an optional component of the checker. The options list defines any user configurable options. It has the following format:

options = (
    ("option-symbol", {"argparse-like-kwarg": "value"}),
)

  • The option-symbol is a unique name for the option. This is used on the command line and in config files. The hyphen is replaced by an underscore when used in the checker, similarly to how you would use argparse.Namespace:

    if not self.linter.config.ignore_ints:
    ...

Next we'll track when we enter and leave a function.

def __init__(self, linter: Optional["PyLinter"] = None) -> None:
    super().__init__(linter)
    self._function_stack = []

def visit_functiondef(self, node: nodes.FunctionDef) -> None:
    self._function_stack.append([])

def leave_functiondef(self, node: nodes.FunctionDef) -> None:
    self._function_stack.pop()

In the constructor we initialise a stack to keep a list of return nodes for each function. An AST checker is a visitor, and should implement visit_<lowered class name> or leave_<lowered class name> methods for the nodes it's interested in. In this case we have implemented visit_functiondef and leave_functiondef to add a new list of return nodes for this function, and to remove the list of return nodes when we leave the function.

Finally we'll implement the check. We will define a visit_return function, which is called with an .astroid.nodes.Return node.

We'll need to be able to figure out what attributes an .astroid.nodes.Return node has available. We can use astroid.extract_node for this:

>>> node = astroid.extract_node("return 5")
>>> node
<Return l.1 at 0x7efe62196390>
>>> help(node)
>>> node.value
<Const.int l.1 at 0x7efe62196ef0>

We could also construct a more complete example:

>>> node_a, node_b = astroid.extract_node("""
... def test():
...     if True:
...         return 5 #@
...     return 5 #@
... """)
>>> node_a.value
<Const.int l.4 at 0x7efe621a74e0>
>>> node_a.value.value
5
>>> node_a.value.value == node_b.value.value
True

For astroid.extract_node, you can use #@ at the end of a line to choose which statements will be extracted into nodes.

For more information on astroid.extract_node, see the astroid documentation.

Now we know how to use the astroid node, we can implement our check.

def visit_return(self, node: nodes.Return) -> None:
    if not isinstance(node.value, nodes.Const):
        return
    for other_return in self._function_stack[-1]:
        if node.value.value == other_return.value.value and not (
            self.linter.config.ignore_ints and node.value.pytype() == int
        ):
            self.add_message("non-unique-returns", node=node)

    self._function_stack[-1].append(node)

Once we have established that the source code has failed our check, we use ~.BaseChecker.add_message to emit our failure message.

Finally, we need to register the checker with pylint. Add the register function to the top level of the file.

def register(linter: "PyLinter") -> None:
    """This required method auto registers the checker during initialization.
    :param linter: The linter to register the checker to.
    """
    linter.register_checker(UniqueReturnChecker(linter))

We are now ready to debug and test our checker!

Debugging a Checker#

It is very simple to get to a point where we can use pdb. We'll need a small test case. Put the following into a Python file:

def test():
    if True:
        return 5
    return 5

def test2():
    if True:
        return 1
    return 5

After inserting pdb into our checker and installing it, we can run pylint with only our checker:

$ pylint --load-plugins=my_plugin --disable=all --enable=non-unique-returns test.py
(Pdb)

Now we can debug our checker!

Note

my_plugin refers to a module called my_plugin.py. The preferred way of making this plugin available to pylint is by installing it as a package. This can be done either from a packaging index like PyPI or by installing it from a local source such as with pip install.

Alternatively, the plugin module can be made available to pylint by putting this module's parent directory in your PYTHONPATH environment variable.

If your pylint config has an init-hook that modifies sys.path to include the module's parent directory, this will also work, but only if either:

  • the init-hook and the load-plugins list are both defined in a configuration file, or...

  • the init-hook is passed as a command-line argument and the load-plugins list is in the configuration file

So, you cannot load a custom plugin by modifying sys.path if you supply the init-hook in a configuration file, but pass the module name in via --load-plugins on the command line. This is because pylint loads plugins specified on command line before loading any configuration from other sources.

Defining a Message#

Pylint message is defined using the following format:

msgs = {
    "E0401": ( # message id
     "Unable to import %s", # template of displayed message
     "import-error", # message symbol
     "Used when pylint has been unable to import a module.",  # Message description
     { # Additional parameters:
          # message control support for the old names of the messages:
         "old_names": [("F0401", "old-import-error")]
         "minversion": (3, 5), # No check under this version
         "maxversion": (3, 7), # No check above this version
     },
 ),

The message is then formatted using the args parameter from add_message i.e. in self.add_message("import-error", args=module_we_cant_import, node=importnode), the value in module_we_cant_import say patglib will be interpolled and the final result will be: Unable to import patglib

  • The message-id should be a 4-digit number, prefixed with a message category. There are multiple message categories, these being C, W, E, F, R, standing for Convention, Warning, Error, Fatal and Refactoring. The 4 digits should not conflict with existing checkers and the first 2 digits should consistent across the checker (except shared messages).

  • The displayed-message is used for displaying the message to the user, once it is emitted.

  • The message-symbol is an alias of the message id and it can be used wherever the message id can be used.

  • The message-help is used when calling pylint --help-msg.

Optionally message can contain optional extra options:

  • The old_names option permits to change the message id or symbol of a message without breaking the message control used on the old messages by users. The option is specified as a list of tuples (message-id, old-message-symbol) e.g. {"old_names": [("F0401", "old-import-error")]}. The symbol / msgid association must be unique so if you're changing the message id the symbol also need to change and you can generally use the old- prefix for that.

  • The minversion or maxversion options specify minimum or maximum version of python relevant for this message. The option value is specified as tuple with major version number as first number and minor version number as second number e.g. {"minversion": (3, 5)}

  • The shared option enables sharing message between multiple checkers. As mentioned previously, normally the message cannot be shared between multiple checkers. To allow having message shared between multiple checkers, the shared option must be set to True.

Parallelize a Checker#

BaseChecker has two methods get_map_data and reduce_map_data that permit to parallelize the checks when used with the -j option. If a checker actually needs to reduce data it should define get_map_data as returning something different than None and let its reduce_map_data handle a list of the types returned by get_map_data.

An example can be seen by looking at pylint/checkers/similar.py.

Testing a Checker#

Pylint is very well suited to test driven development. You can implement the template of the checker, produce all of your test cases and check that they fail, implement the checker, then check that all of your test cases work.

Pylint provides a pylint.testutils.CheckerTestCase to make test cases very simple. We can use the example code that we used for debugging as our test cases.

import astroid
import my_plugin
import pylint.testutils


class TestUniqueReturnChecker(pylint.testutils.CheckerTestCase):
    CHECKER_CLASS = my_plugin.UniqueReturnChecker

    def test_finds_non_unique_ints(self):
        func_node, return_node_a, return_node_b = astroid.extract_node("""
        def test(): #@
            if True:
                return 5 #@
            return 5 #@
        """)

        self.checker.visit_functiondef(func_node)
        self.checker.visit_return(return_node_a)
        with self.assertAddsMessages(
            pylint.testutils.MessageTest(
                msg_id="non-unique-returns",
                node=return_node_b,
            ),
        ):
            self.checker.visit_return(return_node_b)

    def test_ignores_unique_ints(self):
        func_node, return_node_a, return_node_b = astroid.extract_node("""
        def test(): #@
            if True:
                return 1 #@
            return 5 #@
        """)

        with self.assertNoMessages():
            self.checker.visit_functiondef(func_node)
            self.checker.visit_return(return_node_a)
            self.checker.visit_return(return_node_b)

Once again we are using astroid.extract_node to construct our test cases. pylint.testutils.CheckerTestCase has created the linter and checker for us, we simply simulate a traversal of the AST tree using the nodes that we are interested in.